Metadata-Version: 1.2
Name: adjsim
Version: 2.0.0
Summary: An Agent Based Modelling Engine tailored for Reinforcement Learning.
Home-page: https://github.com/SeverTopan/AdjSim
Author: Sever Topan
Author-email: UNKNOWN
License: GPL-3.0
Description: # AdjSim Simulation Framework
        [![Build Status](https://travis-ci.org/SeverTopan/AdjSim.svg?branch=master)](https://travis-ci.org/SeverTopan/AdjSim) [![Coverage Status](https://coveralls.io/repos/github/SeverTopan/AdjSim/badge.svg?branch=master)](https://coveralls.io/github/SeverTopan/AdjSim?branch=master) [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
        
        A simulation framework. Intended for simulation of ecosystems.
        
        
        > Designed and developed by Sever Topan
        
        ## Features
        
        ### Engine
        
        At its core, AdjSim is an agent-based modelling engine. It allows users to define simulation environments through which agents interact through ability casting and timestep iteration. The framework is targeted towards agents that behave intelligently, for example a bacterium chasing down food. However, the framework is extremely flexible - from enabling physics simulation to defining an environment in which [Conway's Game of Life](https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life) plays out! AdjSim aims to be a foundational architecture on top of which reinforcement learning can be built.
        
        ### Graphical Simulation Representation
        
        The simulation can be viewed in real time as it unfolds, with graphics are rendered and animated using PyQt5. Below are  four of the distinct demos packadged with AdjSim, ranging from bacteria to moon system simulation.
        
         | ![Bacteria Demo](https://raw.githubusercontent.com/SeverTopan/AdjSim/master/demo/images/readme_bacteria.png)| ![Predator Prey Demo](https://raw.githubusercontent.com/SeverTopan/AdjSim/master/demo/images/readme_predator_prey.png) |
        |:-------------:|:-------------:|
        | ![GOL Demo](https://raw.githubusercontent.com/SeverTopan/AdjSim/master/demo/images/readme_game_of_life.png) | ![Jupiter Demo](https://raw.githubusercontent.com/SeverTopan/AdjSim/master/demo/images/readme_jupiter_moon_system.png) |
        
        ### Post Simulation Analysis Tools
        
        Agent properties can be marked for tracking during simulation, allowing for viewing the results of these values once the simulation completes. For example, we can track the population of each different type of agent, or the efficacy of the agent's ability to meet its intelligence module-defined goals.
        
        | ![QLearning Graph](https://raw.githubusercontent.com/SeverTopan/AdjSim/master/demo/images/readme_individual_learning.png)| ![Predator Prey Graph](https://raw.githubusercontent.com/SeverTopan/AdjSim/master/demo/images/readme_predator_prey_population.png) |
        |:-------------:|:-------------:|
        
        ### Intelligence Module
        
        Perhaps the most computationally interesting aspect of AdjSim lies in its intelligence module. It allows agents to set goals (for example, the goal of a bacterium may be to maximize its calories), and assess its actions in terms of its ability to meet its goals. This allows the agents to learn which actions are best used in a given situation. Currently the intelligence module implements [Q-Learning](https://en.wikipedia.org/wiki/Q-learning), but more advanced reinforcement learning techniques are coming soon!
        
        ## Installing and Running AdjSim
        
        It is reccommended to run AdjSim using virtual python environments provided by Anaconda or Pip. The following describes the installation procedure for each of these.
        
        Clone the GitHub repository.
        
             git clone https://github.com/SeverTopan/AdjSim
        
        Make sure Python 3.5 or greater are installed, then create a new environment with it.
        
            # If using Virtualenv
            virtualenv --python=/usr/bin/python3.6 venv
            source venv/bin/activate
        
            # If using Anaconda
            conda create --name adjsim python=3.6
            activate adjsim
        
        Install Dependencies.
        
             python setup.py install
        
        Note: If you run into trouble importing PyQt5 when installing using the setup.py file, try using
        
            pip install -e .
        
        
        
Keywords: agent based modelling ABM reinforcement learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.5, <3.7
